27 нояб. 2022 г. · Specifically, we provide the first bound of adversarial Rademacher complexity of deep neural networks. Our approach is based on covering numbers ...

The Rademacher complexity of a feed-forward neural network can be bounded recursively by considering each layer at a time. A bound that can be used for the ...

Rademacher complexity is a capacity measure that captures the ability of functions in a function class to fit random labels which increases with the complexity ...

17 окт. 2018 г. · In the last session we introduced an interesting phenomenon that occures during the training of a two-layer feed-forward neural network and ...

8 авг. 2022 г. · We show that the Rademacher complexity-based approach can generate non-vacuous generalisation bounds on Convolutional Neural Networks (CNNs) for ...

26 мар. 2024 г. · One approach focuses on determining solutions to partial differential equations (PDEs) for fixed PDE and boundary conditions and includes the ...

19 янв. 2021 г. · Rademacher complexity of quantum circuits. Part III: Summary and further direction. Page 3. Preliminary: Neural networks. Example: Feedforward ...

22 окт. 2024 г. · ... This hierarchical learning enables the DNN to automatically discover abstract patterns in the input data, leading to a generalized ...

The representation allows an arbitrary large m, and thus can handle continuous deep neural networks. Page 35. 35. Rademacher Complexity of L1 Regularized DNN.

One of the major theoretical challenges in machine learning is to understand, in a high dimensional setting, the generalization error for deep neural networks, ...